Automated Classification of Normal and Abnormal Leukocytes

Brenner, John F.; Neurath, Peter W.; Selles, W D; Necheles, Thomas F.; Gelsema, E. S.; Vastola, E.F.

doi:10.1177/22.7.697

Cited by 50 publications

(9 citation statements)

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“…Bacus [28], Green [29], Kruse [30], and Zack [31] have shown that a sampling density of about 0.5 p is necessary and sufficient for the analysis of erythrocyte morphology. Bacus [32], Young [33], [34], Brenner [35], Prewitt [19], Ingram [36], and Tyko [37] have also seemed to confirm this figure for leukocyte analysis. In the analysis of exfoliative cervical cytology Bartels [38] has shown through careful studies that 0.5 p sampling is necessary for accurate cell classification.…”

Section: Reinventing the Microscopementioning

confidence: 66%

SSAM: Solid-State Automated Microscope

Young

Balasubramanian²,

Dunbar

et al. 1982

IEEE Trans. Biomed. Eng.

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Abstract-A new microscope system that is designed to provide images for a computer has been built and tested. This system differs from previous computerized microscopes in that the fundamental design parameters have been tuned to the computer as the receiver of the image instead of the human visual system. This solid-state automated microscope system (SSAM) simultaneously provides wide-field (2 mm), high-resolution (0.5 M), high signal-to-noise images (>53 dB) at data rates of 5 X 106 pixels/s. Various methods have been developed and used to test the design specifications of the system against the actual performance.

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Section: Reinventing the Microscopementioning

confidence: 66%

SSAM: Solid-State Automated Microscope

Young

Balasubramanian²,

Dunbar

et al. 1982

IEEE Trans. Biomed. Eng.

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“…The percentages of correct classification calculated from these reports are 92.6% (Hematrak) and 95.9% (LARC). In a laboratory experiment on WBC differentiation (Brenner et a/. 1974) a correct classification was obtained in 96.8';/,.…”

Section: Discussionmentioning

confidence: 99%

Relevance of morphometric parameters for the classification of normal white blood cells*

Bins¹,

Landeweerd

Gelsema

et al. 2008

Clinical &Amp; Laboratory Haematology

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Summary An interactive morphometry system (Leitz T. A. S.) was used to extract 39 parameters from 1506 cells of five normal WBC classes. The data set was split in a learning set and a test set, and the value of each single parameter and of combinations of parameters was assessed with the interactive pattern recognition system ISPAHAN. A combination of nine densitogram parameters gave a correct classification in 92.6%;adding four geometrical and seven counting densitogram parameters improved this to 98.5%. It appeared that simple measurements are sufficient for a correct differentiation into the normal white blood cell types. In a pilot study of 40 normal myeloid bone marrow cells of four classes a correct discrimination between mature and immature cells was reached in 93%. Extraction of parameters with the T. A. S. and their subsequent evaluation with ISPAHAN appeared to be an efficient method of white blood cell classification probably including immature cell types.

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“…An example of such considerations may be seen in the presentation of results in a system for the classification of immature white blood cells [7]. There, the "extended diagonal" expresses in a crude manner the fact that the assignment to a neighboring class (e.g., assigning a myelocyte to the class of metamyelocytes) is not regarded as a severe error.…”

Section: Possible Future Developmentsmentioning

confidence: 99%

Automated Classification of Normal and Abnormal Leukocytes

Cited by 50 publications

References 0 publications

SSAM: Solid-State Automated Microscope

SSAM: Solid-State Automated Microscope

Relevance of morphometric parameters for the classification of normal white blood cells*

Pattern Recognition and Artificial Intelligence in Medical Research and Clinical Practice

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